Yazar "Polat, Huseyin" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Effect of seasonal water temperature variation on the blood serums thyroid hormone levels of juvenile chub fishes (Squalius cappadocicus)
    (TAYLOR & FRANCIS LTD, 2020) Ozeren, Saniye C.; Kankilic, Gokben B.; Erkmen, Belda; Polat, Huseyin; Pehlivan, Erkan
    In this study, the seasonal change (varying with water temperature) of thyroid hormones [Total triiodothyronine (TT3) and Total thyroxine (TT4)] in the blood serums of juvenile chub fish (Squalius cappadocicus) has been investigated. The research has been conducted on the chub fish caught in Melendiz Stream (Aksaray) at different times (2010 - April, June, July, and November; 2011 - February and March). As a result of the hormonal analyses on blood serums, TT3 and TT4 levels have shown meaningful changes (p < 0.05) inversely proportional to the water temperature. It made us think that this change is due to adaptation decreased/increased metabolic (energy) activity provided by the fishes against temperature stress.
  • [ X ]
    Öğe
    Improving detection and classification of diabetic retinopathy using CUDA and Mask RCNN
    (Springer London Ltd, 2023) Erciyas, Abdussamed; Barisci, Necaattin; Unver, Halil Murat; Polat, Huseyin
    Diabetic retinopathy (DR) is an eye disease caused by diabetes and can progress to certain degrees. Because DR's the final stage can cause blindness, early detection is crucial to prevent visual disturbances. With the development of GPU technology, image classification and object detection can be done faster. Particularly on medical images, these processes play an important role in disease detection. In this work, we improved our previous work to detect diabetic retinopathy using Faster RCNN and attention layer. In the detection phase, firstly non-used area of DR image was extracted using compute unified device architecture with gradient-based edge detection method. Then Mask RCNN was used instead of faster region-based convolutional neural networks (Faster RCNN) to detect lesion areas more successful. With the proposed method, more successful results were obtained than the our previous work in DenseNet, MobileNet and ResNet networks. In addition, more successful results were obtained than other works in the literature in ACC and AUC metrics obtained by using VGG19.